惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

V
Visual Studio Blog
N
Netflix TechBlog - Medium
GbyAI
GbyAI
大猫的无限游戏
大猫的无限游戏
博客园 - 三生石上(FineUI控件)
T
Tailwind CSS Blog
IT之家
IT之家
博客园 - Franky
雷峰网
雷峰网
博客园 - 聂微东
腾讯CDC
M
MIT News - Artificial intelligence
B
Blog RSS Feed
博客园_首页
罗磊的独立博客
S
SegmentFault 最新的问题
I
InfoQ
博客园 - 叶小钗
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
阮一峰的网络日志
阮一峰的网络日志
D
Docker
宝玉的分享
宝玉的分享
B
Blog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报

cs.DC updates on arXiv.org

DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Efficient, VRAM-Constrained xLM Inference on Clients Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations ITAS: A Multi-Agent Architecture for LLM-Based Intelligent Tutoring Latency and Cost of Multi-Agent Intelligent Tutoring at Scale TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning Usable Agent Discovery for Decentralized AI Systems Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning Promoting Simple Agents: Ensemble Methods for Event-Log Prediction GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing Federated Learning over Blockchain-Enabled Cloud Infrastructure Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not? Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
Performance analysis and Optimisation of the Met Unified ...
Karthee Sivalingam, Grenville Lister, Bryan Lawrence · 2015-11-12 · via cs.DC updates on arXiv.org

The Unified Model (UM) code supports simulation of weather, climate and earth system processes. It is primarily developed by the UK Met Office, but in recent years a wider community of users and developers have grown around the code. Here we present results from the optimisation work carried out by the UK National Centre for Atmospheric Science (NCAS) for a high resolution configuration (N512 $\approx$ 25km) on the UK ARCHER supercomputer, a Cray XC-30. On ARCHER, we use Cray Performance Analysis Tools (CrayPAT) to analyse the performance of UM and then Cray Reveal to identify and parallelise serial loops using OpenMP directives. We compare performance of the optimised version at a range of scales, and with a range of optimisations, including altered MPI rank placement, and addition of OpenMP directives. It is seen that improvements in MPI configuration yield performance improvements of between 5 and 12\%, and the added OpenMP directives yield an additional 5-16\% speedup. We also identify further code optimisations which could yield yet greater improvement in performance. We note that speedup gained using addition of OpenMP directives does not result in improved performance on the IBM Power platform where much of the code has been developed. This suggests that performance gains on future heterogeneous architectures will be hard to port. Nonetheless, it is clear that the investment of months in analysis and optimisation has yielded performance gains that correspond to the saving of tens of millions of core-hours on current climate projects.